5,052 research outputs found
Efficiently decoding Reed-Muller codes from random errors
Reed-Muller codes encode an -variate polynomial of degree by
evaluating it on all points in . We denote this code by .
The minimal distance of is and so it cannot correct more
than half that number of errors in the worst case. For random errors one may
hope for a better result.
In this work we give an efficient algorithm (in the block length ) for
decoding random errors in Reed-Muller codes far beyond the minimal distance.
Specifically, for low rate codes (of degree ) we can correct a
random set of errors with high probability. For high rate codes
(of degree for ), we can correct roughly
errors.
More generally, for any integer , our algorithm can correct any error
pattern in for which the same erasure pattern can be corrected
in . The results above are obtained by applying recent results
of Abbe, Shpilka and Wigderson (STOC, 2015), Kumar and Pfister (2015) and
Kudekar et al. (2015) regarding the ability of Reed-Muller codes to correct
random erasures.
The algorithm is based on solving a carefully defined set of linear equations
and thus it is significantly different than other algorithms for decoding
Reed-Muller codes that are based on the recursive structure of the code. It can
be seen as a more explicit proof of a result of Abbe et al. that shows a
reduction from correcting erasures to correcting errors, and it also bares some
similarities with the famous Berlekamp-Welch algorithm for decoding
Reed-Solomon codes.Comment: 18 pages, 2 figure
Reed-Muller codes for random erasures and errors
This paper studies the parameters for which Reed-Muller (RM) codes over
can correct random erasures and random errors with high probability,
and in particular when can they achieve capacity for these two classical
channels. Necessarily, the paper also studies properties of evaluations of
multi-variate polynomials on random sets of inputs.
For erasures, we prove that RM codes achieve capacity both for very high rate
and very low rate regimes. For errors, we prove that RM codes achieve capacity
for very low rate regimes, and for very high rates, we show that they can
uniquely decode at about square root of the number of errors at capacity.
The proofs of these four results are based on different techniques, which we
find interesting in their own right. In particular, we study the following
questions about , the matrix whose rows are truth tables of all
monomials of degree in variables. What is the most (resp. least)
number of random columns in that define a submatrix having full column
rank (resp. full row rank) with high probability? We obtain tight bounds for
very small (resp. very large) degrees , which we use to show that RM codes
achieve capacity for erasures in these regimes.
Our decoding from random errors follows from the following novel reduction.
For every linear code of sufficiently high rate we construct a new code
, also of very high rate, such that for every subset of coordinates, if
can recover from erasures in , then can recover from errors in .
Specializing this to RM codes and using our results for erasures imply our
result on unique decoding of RM codes at high rate.
Finally, two of our capacity achieving results require tight bounds on the
weight distribution of RM codes. We obtain such bounds extending the recent
\cite{KLP} bounds from constant degree to linear degree polynomials
A Storage-Efficient and Robust Private Information Retrieval Scheme Allowing Few Servers
Since the concept of locally decodable codes was introduced by Katz and
Trevisan in 2000, it is well-known that information the-oretically secure
private information retrieval schemes can be built using locally decodable
codes. In this paper, we construct a Byzantine ro-bust PIR scheme using the
multiplicity codes introduced by Kopparty et al. Our main contributions are on
the one hand to avoid full replica-tion of the database on each server; this
significantly reduces the global redundancy. On the other hand, to have a much
lower locality in the PIR context than in the LDC context. This shows that
there exists two different notions: LDC-locality and PIR-locality. This is made
possible by exploiting geometric properties of multiplicity codes
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